Mapping of transcriptome changes in cellular function and disease has been transformed by technological advances over the last two decades, from microarrays (Schena et al., 1995) to next-generation sequencing and single cell studies (Shendure et al., 2017). However, interrogating the function of individual transcript dynamics and establishing causal linkages between observed transcriptional changes and cellular phenotype requires the ability to actively control or modulate desired transcripts.
DNA engineering technologies such as CRISPR-Cas9 (Doudna and Charpentier, 2014; Hsu et al., 2014) enable researchers to dissect the function of specific genetic elements or correct disease-causing mutations. However, simple and scalable tools to study and manipulate RNA lag significantly behind their DNA counterparts. Existing RNA interference technologies, which enable cleavage or inhibition of desired transcripts, have significant off-target effects and remain challenging engineering targets due to their key role in endogenous processes (Birmingham et al., 2006; Jackson et al., 2003). As a result, methods for studying the functional role of RNAs directly have remained limited.
One of the key restrictions in RNA engineering has been the lack of RNA-binding domains that can be easily retargeted and introduced into target cells. The MS2 RNA-binding domain, for example, recognizes an invariant 21-nucleotide (nt) RNA sequence (Peabody, 1993), therefore requiring genomic modification to tag a desired transcript. Pumilio homology domains possess modular repeats with each protein module recognizing a separate RNA base, but they can only be targeted to short 8 nt RNA sequences (Cheong and Hall, 2006). While previously characterized type II (Batra et al., 2017; O'Connell et al., 2014) and VI (Abudayyeh et al., 2016; East-Seletsky et al., 2016) CRISPR-Cas systems can be reprogrammed to recognize 20-30 nt RNAs, their large size (˜1200 amino acids, aa) makes it difficult to package into AAV for primary cell and in vivo delivery.